Methods, systems and apparatuses for detecting increased risk of sudden death

ABSTRACT

Methods, systems, and apparatuses for detecting seizure events are disclosed, including a system for identification of an increased risk of a severe neurological event. The system may include an electroencephalogram (“EEG”) monitoring unit configured to collect EEG data from the patient during at least a postictal phase of one or more seizures and a processing unit configured to receive the EEG data from the EEG monitoring unit. The processing unit is configured to detect postictal EEG suppression from the EEG data and to identify the increased risk of the severe neurological event based on the detected postictal EEG suppression. Other embodiments are described and claimed.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to systems, methods, and apparatuses for detecting medical conditions. More particularly, the disclosure relates to systems, methods and apparatuses for detecting medical conditions relating to seizures, especially conditions that may place a patient experiencing a seizure at greater risk of sudden death.

BACKGROUND OF THE DISCLOSURE

The embodiments described herein relate generally to the field of medical detection systems for patients experiencing seizures. “A seizure is an abnormal, unregulated electrical charge that occurs within the brain's cortical gray matter and transiently interrupts normal brain function.” The Merck Manual of Diagnosis and Therapy, 1822 (M. Beers Editor in Chief, 18^(th) ed. 2006) (“Merck Manual”). Epilepsy is a chronic disease characterized by such seizures, but not caused by an event such as a stroke, drug use or physical injury. Seizures may vary in frequency and scope and may range from involving no impairment of consciousness at all to complete loss of consciousness. Typically, a seizure resolves within a few minutes and extraordinary medical intervention, other than that needed for the comfort of the patient and to promote unobstructed breathing, is not needed. (See, generally, Merck Manual at 1822-1827, incorporated herein by reference.)

But in some cases, a seizure may lead to death. Asphyxia is an impairment or absence of the oxygen and carbon dioxide exchange in the body, which can occur, for example, during suffocation. Asphyxia is considered to be the leading cause of Sudden Unexplained Death in Epileptic Patients (“SUDEP”) and may indeed trigger SUDEP. But the mechanism and relationship of SUDEP with cardiorespiratory and cerebral function has been poorly understood. SUDEP does not occur during or, generally, immediately after an initial phase of a seizure but as the patient appears to be recovering from the seizure. In addition, SUDEP may occur at night, while the patient is sleeping. Such sudden unexplained death is not necessarily limited to seizure patients and may be underreported in the general population. But seizure patients, including those with epilepsy, seem to be at a higher risk for sudden unexplained death than the general population.

In a typical seizure condition, there are three phases: ictal, post ictal (or “postictal”), and interictal. The ictal phase is the initial portion of the seizure, where a patient may display symptoms, if any, such as convulsions. Generally speaking, the interictal phase is the period between seizures when the patient has substantially recovered.

A postictal phase takes place immediately after the ictal phase of the seizure, where symptoms have subsided, but the patient has not yet returned to normal. During the postictal period, the patient may be relaxed or lying down and may appear to be sleeping. In the postictal period, the patient's heart rate may typically take a few minutes to return to the patient's non-seizure baseline. The same is true of the patient's electrocardiogram (“EKG” or “ECG”) measurements, if the patient should happen to be undergoing EKG testing at the time of the seizure. EKG measurements provide a record of the heart's integrated action over a period of time. Cardiac and respiratory readings for the patient soon appear to be normal as the patient progresses in the postictal period. Such measurements and readings, along with visual observation, would support a view that a patient is coming out of the seizure in a normal fashion and is not at risk for SUDEP. One might thus conclude that no medical intervention is necessary. But in some cases, such measurements, readings and observations would be deceptive and the patient is at risk of SUDEP.

If a condition in a patient that leads to an increased risk of SUDEP can be detected, timely measures may be taken that would reduce that risk and possibly save the patient.

Accordingly, a need is present for methods, systems and apparatuses to detect one or more conditions in a patient that may lead to SUDEP and/or overcome issues discussed above.

SUMMARY

The embodiments of the disclosure described herein include a system for identification of an increased risk of a severe neurological event. The system may include an electroencephalogram (“EEG”) monitoring unit configured to collect EEG data from the patient during at least a postictal phase of one or more seizures and a processing unit configured to receive the EEG data from the EEG monitoring unit. The processing unit is configured to detect postictal EEG suppression from the EEG data and to identify the increased risk of the severe neurological event based on the detected postictal EEG suppression.

The embodiments of the disclosure described herein also include a system for identification of an increased risk of a severe neurological event. The system includes an electroencephalogram (“EEG”) monitoring unit, a processing unit, a respiratory monitoring unit and an electrocardiogram (“EKG”) monitoring unit. The electroencephalogram (“EEG”) monitoring unit is configured to collect EEG data from the patient's brain during at least a postictal phase of one or more seizures. The processing unit is configured to receive the EEG data from the EEG monitoring unit and to detect postictal EEG suppression from the EEG data when the EEG data crosses an EEG threshold for at least a predetermined time period. The respiratory monitoring unit is configured to collect respiratory data from the patient's respiration during at least the postictal phase of the one or more seizures in response to detection of the postictal EEG suppression. The processing unit receives the respiratory data from the respiratory monitoring unit and determines whether the respiratory data crosses a respiratory threshold. The electrocardiogram (“EKG”) monitoring unit is configured to collect EKG data for the patient's heart. The processing unit is configured to receive the EKG data from the EKG monitoring unit and to determine whether the EKG data crosses an EKG threshold. The processing unit is further configured to determine whether the EKG data crosses the EKG threshold in response to a determination that the respiration data crosses the respiration threshold. The processor may identify the increased risk of the severe neurological event based on the detected postictal EEG suppression, the determination that the respiration data crosses the respiration threshold, and the determination that the EKG data crosses the EKG threshold.

The embodiments of the disclosure described herein also include a method for detecting a condition in a patient that poses an increased risk of sudden unexplained death in epilepsy (SUDEP), including collecting encephalogram (“EEG”) data from a patient during at least a postictal phase of one or more seizures, and determining whether the EEG data indicates postictal EEG suppression when the EEG data crosses an EEG threshold.

Other aspects and advantages of the embodiments described herein will become apparent from the following description and the accompanying drawings, illustrating the principles of the embodiments by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the present disclosure will become apparent from the appended claims, the following detailed description of one or more example embodiments, and the corresponding figures.

FIG. 1 is a graph depicting EEG measurements where a first patient is recovering normally and a second and third patient start exhibiting EEG suppression indicative of an increased risk of SUDEP.

FIG. 2 is a depiction of relationships between some conditions that may be related to SUDEP.

FIGS. 3A-C are depictions of flowcharts for a process and methods in accordance with one or more embodiments of the present disclosure.

FIG. 4 is a depiction of a system for a patient in a medical facility in accordance with one or more embodiments of the present disclosure.

FIG. 5 is a depiction of a system for a patient outside of a medical facility, in accordance with one or more embodiments of the present disclosure.

While the disclosure is subject to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and the accompanying detailed description. It should be understood, however, that the drawings and detailed description are not intended to limit the invention to the particular embodiments. This disclosure is instead intended to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims.

DETAILED DESCRIPTION

The drawing figures are not necessarily to scale and certain features may be shown exaggerated in scale or in somewhat generalized or schematic form in the interest of clarity and conciseness. In the description which follows, like parts may be marked throughout the specification and drawing with the same reference numerals. The foregoing description of the figures is provided for a more complete understanding of the drawings. It should be understood, however, that the embodiments are not limited to the precise arrangements and configurations shown. Although the design and use of various embodiments are discussed in detail below, it should be appreciated that the present disclosure provides many inventive concepts that may be embodied in a wide variety of contexts. The specific aspects and embodiments discussed herein are merely illustrative of ways to make and use the disclosure, and do not limit the scope of the disclosure. It would be impossible or impractical to include all of the possible embodiments and contexts in this disclosure. Upon reading this disclosure, many alternative embodiments of the present disclosure will be apparent to persons of ordinary skill in the art.

TABLE 1 Mean EEG Clinical EKG Postictal Seizure Seizure Seizure Breathing Identification Duration Duration Duration (Y/N) 1 (10 sz) 2.24 min. 1.96 min  4.85 min  Y 2 (11 sz) 1.89 min. 1.69 min. 4.18 min. Y 3 (7 sz)  1.77 min  1.71 min. 4.02 min. Y

Table 1 compares seizure duration as determined by different types of measurements. These measurements include EKG measurements, clinical measurements which are based on visual observations of the patient, and electroencephalogram (“EEG”) readings. In contrast to EKG devices which measure heart activity, EEG devices measure brain activity, often in several parts of the brain at once. Note that the mean EEG seizure duration differs from both the clinical and EKG measurements in these examples being longer than clinical duration and shorter than EKG seizure duration. This is because the brain behavior of patients having seizures can be quite different from the patient's cardiac and respiratory behavior. The EEG indications of seizure are considered the most accurate and EEG variation will typically begin a few seconds before a patient begins experiencing physical symptoms, i.e. when the clinical duration begins. The EKG measurements in the EKG seizure duration column indicate that the heart generally takes longer to return to baseline than either the EEG measurements or the clinical observations would indicate. The far right hand column of Table 1 indicates that postictal breathing was present.

FIG. 1 is a graph 100 depicting EEG measurements where a first patient is recovering normally and a second and third patient start exhibiting EEG suppression indicative of an increased risk of SUDEP. The graph 100 includes three EEG waveforms. A first EEG waveform 102 shows EEG data of a patient having seizures and recovering normally. A second EEG waveform 104 shows EEG data of a patient having multiple seizures and experiencing at least one EEG suppression during the postical phase of each seizure. A third EEG waveform 106 shows EEG data of a patient having seizures and experiencing multiple EEG suppressions during the postical and interictal phase after a single seizure. Waveforms 102, 104, and 106 each start with a normal EEG signal during an interictal phase. The interictal phase ends at time 108 and the itcal phase begins with the onset of a seizure. The seizure shown in waveforms 102, 104, and 106 ends at time 110 and the patients enter a postictal phase. The EEG waveform 102 shows a normal recovery during the postictal phase. The EEG waveform 104 shows a period of EEG suppression 112 and EEG waveform 106 also shows a period of EEG suppression 114. During EEG suppression periods 112 and 114, the amplitudes of the corresponding EEG waveforms are greatly suppressed and appear flat. These EEG suppression periods indicate a significant reduction in brain activity of the patient. The EEG waveforms 104 and 106 show that the patients EEG signal returns to normal and each of the EEG waveforms 102, 104, and 106 may transition into the interictal phase.

A time lapse 116 is provided to skip to the next seizure occurring in EEG waveforms 102 and 104. The EEG waveform 106 indicates that the patient is still in the interictal phase and that a subsequent seizure has not occurred. The second seizure shown in the EEG waveform 102 ends at time 118 and shows normal recovery during the postictal phase. The second seizure shown in the EEG waveform 104 ends at time 118 and enters an EEG suppression period 120 that is longer than the previous EEG suppression period 112. The EEG waveform 106 transitions from an interictal phase at time 118 and enters an EEG suppression period 122 that is longer than the previous EEG suppression period 114 without any intervening seizures. This increase in the duration of the EEG suppression periods in the EEG waveforms 104 and 106 may be indicative of a progressive or worsening condition that may lead to a severe neurological event, such as SUDEP.

While FIG. 1 depicts one EEG signal for each of the scenarios, EEG readings are typically performed on many channels representing measurements from many EEG sensors. Whether one channel, two or more channels or all channels will be suppressed in a condition leading to SUDEP, or whether there will be a cascading effect, may depend on the particular pathology of the patient or the severity of the patient's current condition. One set of channels may exhibit a suppressive behavior pattern. The suppressive behavior pattern may migrate to another location or could propagate, with additional channels exhibiting the suppressive behavior. Often a higher number of channels being involved indicates that the case may be more serious.

By detecting a period of postictal EEG suppression, one may be able to intervene to assist the patient and prevent SUDEP. But any assistance must be prompt because potential remedies for SUDEP may not be effective unless they are applied in a timely manner. Possible treatments include activating an implanted medical device, such as a neurostimulator described in U.S. Pat. No. 5,304,206, an injection of an appropriate dosage of medicine, CPR and/or defibrillation, use of an external device, such as a helmet for cooling the brain, or EMS (electromagnetic stimulation). The treatment could be increased or changed if the patient does not respond. For example, the neurostimulator could include successive rounds of stimulation at higher currents if the postictal EEG suppression continues. Appropriate medication could be injected via a pump device, with an additional dose being added if the patient is not responsive. Depending on the patient and the treating physician's evaluation, more than one treatment may be used, either in combination or sequentially, for some patients.

FIG. 2 is a depiction of relationships between conditions that may be related to SUDEP 222 and to postictal EEG suppression 220. Conditions which may be related to SUDEP 222 and postictal EEG suppression 220 include postictal apnea 200, postictal hypoxia 210 and postictal bradycardia 215. Postictal apnea 200, which is an absence of breathing, can give rise to both postictal hypoxia 210, which is decreased levels of oxygen in a person, and postictal bradycardia 215, which is a slowing of a person's heart rate. Both postictal hypoxia 210 and postictal bradycardia 215 can in turn increase postictal apnea 200. Postictal hypoxia 210 can also increase postictal bradycardia 215. All of these conditions may also contribute to, or be indicative of, postictal EEG suppression 220 and may be present before, or during, SUDEP 222. This may explain why postictal EEG suppression 220 may also be indicative of SUDEP 222.

FIG. 3A is a depiction of a flowchart for a process in accordance with one or more embodiments of the present disclosure. A patient's EEG readings are monitored 300 during at least postictal portions of a seizure and preferably starting within the ictal period of the seizure. Many techniques could be used to detect an EEG suppression, such as a nonlinear signal analysis or a recurrence quantification analysis. The EEG suppression may last for a relatively long time or it may comprise shorter, re-occurring periods of suppression, interspersed with periods of normal activity. In the latter case, the re-occurring periods of suppression may progressively get worse. While an EEG suppression is most likely during the postictal phase, it may occur at other times as well. In several typical scenarios, the EEG suppression may occur 45-95 minutes after a seizure has occurred. Accordingly, in various embodiments of the disclosure, monitoring may occur outside of the postictal phase or may be continuous. The EEG suppression may be progressive exhibiting a trend of generally increasing duration of successive suppression periods. A single suppression period may occur after each seizure where each suppression period after each successive seizure generally increases in duration. Alternatively, multiple suppression periods may occur after a single seizure exhibiting the trend of generally increasing duration. The multiple suppression periods may be interleaved with non-suppression periods where the non-suppression periods exhibit a trend of generally decreasing duration.

If there is detection 310 of an EEG suppression, then a respiratory or other physiological sensor is activated 315 to measure breathing rate (“BR”) of the patient. If the breathing rate falls 320 below a first predetermined threshold, such as a respiratory threshold, then EKG and/or accelerator measurements 325 may be taken 325 of the patient. If EKG readings are less than a second predetermined threshold 330, such as an EKG threshold, and/or accelerator measurements are less than 330 a third predetermined threshold, then a warning is issued 335 by a warning device. The warning can be audible, visual and/or vibrational. A warning device could also send messages, such as recorded telephone calls or e-mail messages or text messages to designated persons. The messages in one or more embodiments of the present disclosure may include information about measurements done on the patient. The warning may be local in nature, alerting the patient (if conscious) and/or those in immediate attendance upon the patient and/or may be sent to appropriate medical responders not in the immediate vicinity of the patient.

In other embodiments, the breathing rate and/or the EKG measurements may be monitored differently, such as continuously or while EEG measurements are taken.

FIG. 3B is a depiction of one embodiment of a method for detecting a condition in a patient that poses an increased risk of a severe neurological event, such as sudden unexplained death in epilepsy (SUDEP). The method collects EEG data from a patient during at least a postictal phase of one or more seizures at 340. The method further determines whether the EEG data indicates postictal EEG suppression when the EEG data crosses an EEG threshold, at 345, where the postictal EEG suppression is progressive postictal EEG suppression comprised of multiple suppression periods interleaved with non-suppression periods. The postictal EEG suppression may be progressive when the suppression is characterized by a trend of generally increasing duration with time of the multiple suppression periods. The method further activates a warning device when the EEG data indicates postictal EEG suppression for a predetermined period of time at 350.

FIG. 3C is a depiction of another embodiment of a method for detecting a condition in a patient that poses an increased risk of a severe neurological event, such as sudden unexplained death in epilepsy (SUDEP). The method collects EEG data from a patient during at least a postictal phase of one or more seizures, at 355, and determines whether the EEG data indicates postictal EEG suppression when the EEG data crosses an EEG threshold, at 360. The method further collects respiratory data from the patient's respiration, at 365, and determines whether the respiratory data crosses a first threshold, at 370. The method further collects EKG data for the patient's heart, at 375, and determines whether the EKG data crosses a second threshold, at 380. The method activates a warning device when the EEG data indicates postictal EEG suppression for a predetermined period of time, the respiratory data crosses the first threshold, and the EKG data crosses the second threshold, at 385.

FIG. 4 is a depiction of a system for a patient in a medical facility or at home, for use during sleep, for example, in accordance with one or more embodiments of the present disclosure. A patient 400, who may be asleep or between seizures or in an ictal or postictal portion of a seizure, has EEG sensors 405 placed on the patient's body in appropriate locations for taking EEG measurements. The EEG sensors 405 are attached to an EEG monitoring unit 410, via an EEG lead 415. Although two EEG sensors 405 and EEG leads 415 are depicted in FIG. 4, there may be many EEG sensors 405 and EEG leads 415. Although EEG leads 415 are currently used for transmitting information from the EEG sensors 405 to the EEG monitoring unit 410, it may be practical in the future to transmit information from the EEG sensors 405 to the EEG monitoring unit 410 wirelessly. Wearable EEG monitors for personal or home use are becoming available commercially. (See e.g. the HealthPals™ device by Olga Epikhina.)

In alternate embodiments, an EEG helmet or headgear may be used and may include many sensors. Alternatively, in the absence of EEG data or in addition thereto, heart and respiration measurements may be used as surrogate markers. For example, during EEG suppression, respiration may proceed in a pattern from tachycardia, to bradycardia, and back to tachycardia.

In the embodiment depicted in FIG. 4, the patient 400 is also monitored by an EKG monitoring unit 420 having EKG sensors 422 placed on the patient's body to monitor the patient's heart activity and linked to the EKG monitoring unit 420 through one or more EKG leads 425. Although two EKG sensors 422 and EKG leads 425 are depicted in FIG. 4, there may be many EKG sensors 422 and EKG leads 425. While EKG's have traditionally been used in a hospital setting, handheld EKG's for home use are now commercially available, some with only a single EKG sensor 422. Although EKG leads 425 are currently used for transmitting information from the EKG sensors 422 to the EKG monitoring unit 420, it may be practical in the future to transmit information from the EKG sensors 422 to the EKG monitoring unit 420 wirelessly.

Continuing to refer to FIG. 4, in this embodiment of the present disclosure, the respiration of the patient 400 is also monitored through a respiratory monitoring unit 430, which includes one or more respiratory sensors 432 attached to the patient's body 400 through one or more respiratory leads 435. Respiration may be monitored in many ways, for example, by use of a nose clip, by a chest band to determine chest expansion, by measuring the temperature of the inhale versus the temperature of the exhale, indirectly by use of an oximeter, or by impedance measurements using chest electrodes. There are some caveats to using respiration measurements in embodiments of the disclosure, but a treating physician can evaluate these factors in determining how to treat a specific patient. For example, respiration could be subject to natural cycles and certain conditions, such as obstructive sleep apnea or central sleep apnea, may mimic SUDEP conditions, resulting in a false SUDEP warning. But considered carefully, respiration measurements may be helpful in different embodiments of the present disclosure.

Signals from the EEG monitoring unit, the EKG monitor unit and the respiratory monitoring unit are fed through one or more signal lines 438 to a processor 440 (or processing unit). Alternatively, the signals could be sent to the processor 440 in a wireless fashion. The processor 440 runs software preferably capable of determining three conditions, including whether: (1) the EEG measurements indicate a period of postictal suppression; (2) the respiration of the patient has fallen below a first threshold and (3) the EKG measurements have fallen below a second threshold. If all three conditions are met, the processor 440 activates an alarm 445, signaling the need for immediate medical intervention. In alternative embodiments, the processor 440 is set so that it signals the alarm if two of the three conditions are met. For example, if EEG measurements are not available or show no change, but heart rate cycles and respiration cycles become longer, more progressive and more extreme, a warning should be triggered.

In some cases, the alarm may be triggered if just one of the conditions is met or if the EEG suppression is prolonged, even if the other two conditions are not met. The EEG suppression is typically the most specific and earliest marker, and in some embodiments of the present disclosure, detection of EEG suppression may alone trigger the alarm. Severe or progressive deterioration of respiration or heart rate may also warrant alarm activation in some patients.

In alternative embodiments, different alarms or messages may be activated for each of the different kinds of measurements being taken of a patient. In alternative embodiments, other physiological measurements with related thresholds may be substituted for the respiratory measurements and the EKG measurements.

FIG. 5 is a depiction of a system for a patient outside of a medical facility, in accordance with one or more embodiments of the present disclosure. In FIG. 5, a person 500 is wearing a headgear 510 containing one or more sensors, which take EEG measurements of the person 500, preferably on a real time basis. The EEG measurements are sent, preferably wirelessly 515, but alternatively through wired connections, to a one or more EEG monitoring units 520, 530, which may include a remote monitor 520 and/or a local monitor 530, such as a handheld device 530, either or both of which can store and analyze the EEG measurements. Should the person's 500 EEG measurements fall below a predetermined threshold, such as an EEG threshold, alarms or alerts 535 are executed by the handheld device 530 and the remote monitor 520. The EEG threshold may include falling below a level of brain activity for a predetermined time period. Additional alerts 535 could be sent to specified recipients such as medical responders and/or family members and/or other designated persons. The person's location could also be provided to the recipients using a GPS or other locator which might be a function included with the handheld device 530 or provided on a separate device in communication with the handheld device 530.

Continuing to refer to FIG. 5, should no help arrive within a pre-determined period of time and/or the person's EEG measurements remain suppressed during the predetermined period of time, an optional personal medical delivery system 540 could automatically introduce an emergency remedy into the patient's system in an attempt to avert imminent death. The personal medical delivery system 540 could, for example, comprise a neurostimulator, such as that described in U.S. Pat. No. 5,304,206, or an implantable medical device for delivery of a premeasured dosage of medicine, a brain cooling system, CPR and/or defibrillation or EMS (electromagnetic stimulation). The headgear 510 could continue to monitor the patient's condition, with successive or alternative therapy being delivered by the optional personal medical delivery system 540 as needed, until and unless deactivated by medical personnel.

In one or more embodiments of the disclosure, the alert device could also provide written or verbal instructions to those attending to the person. The processor of the present disclosure could maintain a record of measurements made by the system for display, downloading or transmittal to other sites.

In light of the principles and example embodiments described and illustrated herein, it will be recognized that the example embodiments can be modified in arrangement and detail without departing from such principles. Also, the foregoing discussion has focused on particular embodiments, but other configurations are contemplated. In particular, even though expressions such as “in one embodiment,” “in another embodiment,” or the like are used herein, these phrases are meant to generally reference embodiment possibilities, and are not intended to limit the disclosure to particular embodiment configurations. As used herein, these terms may reference the same or different embodiments that are combinable into other embodiments.

Similarly, although example processes have been described with regard to particular operations performed in a particular sequence, numerous modifications could be applied to those processes to derive numerous alternative embodiments of the present invention. For example, alternative embodiments may include processes that use fewer than all of the disclosed operations, processes that use additional operations, and processes in which the individual operations disclosed herein are combined, subdivided, rearranged, or otherwise altered.

This disclosure also described various benefits and advantages that may be provided by various embodiments. One, some, all, or different benefits or advantages may be provided by different embodiments.

In view of the wide variety of useful permutations that may be readily derived from the example embodiments described herein, this detailed description is intended to be illustrative only, and should not be taken as limiting the scope of the invention. What is claimed as the invention, therefore, are all implementations that come within the scope of the following claims, and all equivalents to such implementations. 

What is claimed is:
 1. A system for identification of an increased risk of a severe neurological event, comprising: an electroencephalogram (EEG) monitoring unit configured to receive EEG data generated by one or more EEG sensors adapted to sense neurological activity of a patient during at least a postictal phase of each of one or more seizures; and a processing unit configured to: receive the EEG data from the EEG monitoring unit; detect a plurality of postictal EEG suppression periods interleaved with postictal EEG non-suppression periods during each of at least one postictal phase of the one or more seizures from the EEG data, wherein a postictal EEG suppression period is detected when the EEG data crosses an EEG threshold for at least a predetermined time period; detect a first trend of generally increasing duration with time of the plurality of postictal EEG suppression periods during the postictal phase of one or more seizures; and identify the increased risk of the severe neurological event based on the first trend.
 2. The system of claim 1, wherein the severe neurological event is sudden unexplained death in epilepsy (SUDEP).
 3. The system of claim 1, wherein at least a first EEG suppression period of the plurality of postictal EEG suppression periods occurs after a first seizure and at least a second EEG suppression period of the plurality of postictal EEG suppression periods occurs after a second seizure, the second seizure occurring after the first seizure.
 4. The system of claim 1, wherein a progressive postictal EEG suppression is characterized by the first trend, and wherein the progressive postictal EEG suppression is further characterized by a second trend of generally decreasing duration with time of the plurality of postictal EEG non-suppression periods during the postictal phase of the one or more seizures.
 5. The system of claim 1, further comprising: a respiratory monitoring unit configured to collect respiratory data from the patient, wherein the processing unit is configured to receive the respiratory data from the respiratory monitoring unit and to determine whether a breathing rate determined from the respiratory data crosses a respiration threshold.
 6. The system of claim 5, wherein the processing unit is configured to determine whether the breathing rate crosses the respiration threshold in response to a determination that the EEG data crosses the EEG threshold.
 7. The system of claim 5, further comprising: an electrocardiogram (EKG) monitoring unit configured to collect EKG data from the patient, wherein the processing unit is configured to receive the EKG data from the EKG monitoring unit and to determine whether the EKG data crosses an EKG threshold.
 8. The system of claim 7, wherein the processing unit is configured to determine whether the EKG data crosses the EKG threshold in response to a determination that the breathing rate crosses the respiration threshold.
 9. A method for detecting a condition in a patient that poses an increased risk of sudden unexplained death in epilepsy (SUDEP), the method comprising: collecting, at a processor of a medical device, electroencephalogram (EEG) data from one or more EEG sensors adapted to sense neurological activity of a patient during at least a postictal phase of each of one or more seizures; determining, via the processor, that the EEG data indicates postictal EEG suppression when the EEG data crosses an EEG threshold for at least a predetermined time period; detecting, via the processor, a plurality of postictal EEG suppression periods interleaved with postictal EEG non-suppression periods during each of at least one postictal phase of the one or more seizures from the EEG data; detecting, via the processor, a first trend of generally increasing duration with time of the plurality of postictal EEG suppression periods during the postictal phase of one or more seizures; and identifying, via the processor, the increased risk of the severe neurological event based on the first trend.
 10. The method of claim 9, wherein the predetermined time period is a first predetermined time period, the method further comprising: activating a warning device when the EEG data indicates postictal EEG suppression for a second predetermined period of time.
 11. The method of claim 9, further comprising: collecting respiratory data from the patient; determining whether a breathing rate determined from the respiratory data crosses a first threshold; collecting electrocardiogram (EKG) data patient; and determining whether the EKG data crosses a second threshold.
 12. The method of claim 11, wherein the predetermined time period is a first predetermined time period, the method further comprising: activating a warning device when the EEG data indicates postictal EEG suppression for a second predetermined period of time, the breathing rate crosses the first threshold, and the EKG data crosses the second threshold.
 13. The method of claim 9, wherein the postictal EEG suppression is progressive postictal EEG suppression comprised of multiple suppression periods interleaved with non-suppression periods. 